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Covers the full pipeline: substantive problem framing, consumer utility modeling, identification strategy design, structural estimation (BLP, GMM), field\u002Flab experiments, conjoint analysis, counterfactual simulations, and marketing implications with actionable managerial recommendations.\n---\n\n# Marketing Science Paper Writing\n\n## Overview\n\nMarketing Science papers follow a distinct pipeline: **Substantive Problem → Consumer + Firm Model → Identification Strategy → Estimation\u002FExperiment → Counterfactual Analysis → Marketing Implications**. Unlike MS&E papers that prioritize analytical derivation, marketing papers are evaluated on the **credibility of the empirical identification** and the **managerial actionability of the counterfactual**.\n\nThis skill provides journal-specific guidance for 8 flagship marketing journals, covering structural modeling, causal inference, field experiments, conjoint analysis, and consumer analytics.\n\n**Core Principle**: A marketing paper is an **empirical argument about consumer or firm behavior with testable implications**. Every model, regression, and experiment must answer: \"What should a Chief Marketing Officer do differently because of this finding?\"\n\n---\n\n## When to Use This Skill\n\n- Drafting a paper targeting Marketing Science, JMR, JM, JCR, JAMS, IJRM, QME, or Marketing Letters\n- Designing a structural model of consumer demand (BLP, dynamic structural, discrete choice)\n- Planning identification strategy (IV, DID, RDD, field experiment, lab experiment)\n- Writing up conjoint analysis, demand estimation, or field experiment results\n- Conducting counterfactual policy simulations (merger, tax, product introduction)\n- Formulating substantive marketing implications from empirical results\n- Addressing endogeneity concerns in observational marketing data\n\n---\n\n## Journal Selection Quick Reference\n\n### Tier 1: Top Marketing Journals\n\n| Journal | Core Identity | Ideal Paper Profile |\n|---------|--------------|---------------------|\n| **Marketing Science** (MktSci) | Quantitative, model-driven, structural | Structural model + novel data + counterfactual showing substantive managerial impact. ~35 pages. |\n| **Journal of Marketing Research** (JMR) | Empirical rigor, identification credibility | Well-identified study (experiment\u002Fquasi-experiment) + clear managerial recommendations. ~40 pages. |\n| **Journal of Marketing** (JM) | Broad strategy, conceptual + empirical | Conceptual framework + multi-method validation; strong on marketing strategy. ~40 pages. |\n| **Journal of Consumer Research** (JCR) | Consumer psychology, behavioral | Theory-driven lab\u002Ffield experiments testing psychological mechanisms. ~40 pages. |\n\n### Tier 2: Strong Field Journals\n\n| Journal | Core Identity | Ideal Paper Profile |\n|---------|--------------|---------------------|\n| **JAMS** (J. Academy of Marketing Science) | Broad scope, reviews valued | Conceptual + empirical; strong review papers; AMA-affiliated |\n| **IJRM** (Int. J. Research in Marketing) | European tradition, diverse methods | All methods welcomed; strong special issue culture |\n| **QME** (Quantitative Marketing & Economics) | Structural IO approach | Rigorous structural econometrics; smaller volume, higher technical bar |\n| **Marketing Letters** | Short, sharp empirical | Concise empirical contributions (~20 pages); replication-friendly |\n\n### Decision Flowchart\n\n```\nWhat is your primary method?\n├── Structural econometric model (BLP, dynamic) → Marketing Science, QME\n├── Field experiment (A\u002FB test, RCT) → JMR, Marketing Science\n├── Lab experiment (psychological mechanism) → JCR, JMR\n├── Multi-method empirical (survey + secondary) → JM, JAMS\n├── Conceptual\u002Ftheoretical framework → JM, JAMS, JCR\n└── Short empirical note → Marketing Letters\n```\n\n---\n\n## The Marketing Paper Structure\n\nUnlike IMRAD or MS&E papers, marketing papers have a distinctive structure driven by the identification narrative:\n\n| Section | Typical Pages | Key Content |\n|---------|--------------|-------------|\n| Abstract | 150-200 words | Substantive problem, method, key finding, substantive marketing implication |\n| S1 Introduction | 2-3 pages | Motivating marketing phenomenon → Research gap → Identification approach → Contributions |\n| S2 Conceptual Framework \u002F Model | 3-6 pages | Consumer utility specification, firm objective, market equilibrium, hypotheses |\n| S3 Data & Identification | 4-8 pages | Data sources, sample construction, identification strategy, institutional context |\n| S4 Estimation \u002F Experiment Design | 3-6 pages | Estimator specification, experimental protocol, diagnostic tests, main results |\n| S5 Counterfactuals \u002F Robustness | 3-5 pages | Policy simulations, heterogeneity analysis, alternative specifications, placebo tests |\n| S6 Discussion & Marketing Implications | 1-2 pages | What should marketers DO differently? Theoretical contributions. Limitations. |\n| References | — | 40-80 papers typical |\n| Appendix | Unlimited | Additional robustness, alternative specifications, simulation details |\n\n---\n\n## Methodology Guide by Approach\n\n### 1. Structural Econometric Modeling\n\nSee [references\u002Fstructural-modeling.md](references\u002Fstructural-modeling.md) for comprehensive guidance.\n\n**The BLP Framework** (Berry-Levinsohn-Pakes, 1995)—the workhorse for aggregate demand estimation:\n\n$u_{ijt} = \\alpha_i p_{jt} + \\beta_i x_{jt} + \\xi_{jt} + \\varepsilon_{ijt}$\n\nKey elements:\n- Consumer heterogeneity via random coefficients ($\\alpha_i, \\beta_i$)\n- Price endogeneity addressed via instruments (cost shifters, Hausman instruments, BLP differentiation IVs)\n- Demand inversion via contraction mapping: $\\delta_{jt} \\leftarrow \\delta_{jt} + \\ln s_{jt} - \\ln s_{jt}(\\delta, \\theta)$\n- Supply side: Bertrand-Nash FOCs provide additional GMM moments\n- Counterfactuals: merger simulation, tax incidence, product entry\u002Fexit\n\n**Dynamic Structural Models**:\n- State variables: brand loyalty, inventory, advertising stock, learning\n- Solution: nested fixed point (Rust) or CCP (Hotz-Miller)\n- Applications: brand choice dynamics, new product diffusion, customer relationship management\n\n### 2. Causal Inference \u002F Reduced-Form\n\nSee [references\u002Fcausal-inference.md](references\u002Fcausal-inference.md) for comprehensive guidance.\n\n**Primary Designs in Marketing**:\n\n| Design | Typical Application | Key Reference |\n|--------|-------------------|---------------|\n| **Difference-in-Differences** | Ad campaign effects, store entry, policy changes | Callaway & Sant'Anna (2021); Sun & Abraham (2021) |\n| **Regression Discontinuity** | Price thresholds, eligibility cutoffs, geographic boundaries | Calonico et al. (2014) |\n| **Instrumental Variables** | Price endogeneity, advertising endogeneity | Angrist & Krueger (2001) |\n| **Synthetic Control** | Single-market interventions | Abadie et al. (2010) |\n\n**The Identification Narrative**: Every marketing paper must clearly articulate:\n1. The causal relationship of interest\n2. The source of endogenous variation\n3. The source of exogenous variation (the \"natural experiment\")\n4. The identifying assumptions\n5. How assumptions are tested (balance tests, placebo tests, falsification)\n\n### 3. Field Experiments\n\nSee [references\u002Ffield-experiments.md](references\u002Ffield-experiments.md) for comprehensive guidance.\n\n**Design Elements**:\n- Randomization unit: individual customer, store, DMA (Designated Market Area)\n- Stratification and blocking for balance\n- Power analysis to justify sample size\n- Pre-registration (AEA Registry, AsPredicted, OSF)\n\n**Reporting Standards**:\n- CONSORT-style flow diagram showing participant flow\n- Balance table comparing treatment\u002Fcontrol on pre-treatment characteristics\n- Intent-to-treat (ITT) as primary estimate\n- Treatment-on-the-treated (TOT) via instrumental variables for non-compliance\n- Multiple testing correction (Bonferroni, Holm, or Benjamini-Hochberg)\n\n### 4. Conjoint Analysis\n\nSee [references\u002Fconjoint-analysis.md](references\u002Fconjoint-analysis.md) for comprehensive guidance.\n\n**Design**: Fractional factorial, random designs; incentive-aligned via BDM mechanism\n**Estimation**: Hierarchical Bayes multinomial logit; WTP from part-worths\n**Validation**: Holdout prediction accuracy; face validity of attribute importances\n\n---\n\n## Identification: The Make-or-Break Dimension\n\nMarketing Science and JMR reviewers prioritize identification credibility above all else. A paper without a clearly articulated identification strategy is desk-rejected.\n\n### Identification Checklist\n\n- [ ] Source of exogenous variation is clearly described with institutional detail\n- [ ] First-stage is strong (F > 10 for IV; visual jump for RDD)\n- [ ] Parallel pre-trends are shown visually (event study plot for DID)\n- [ ] Balance table compares treatment\u002Fcontrol on pre-treatment observables\n- [ ] Placebo\u002Ffalsification tests: fake treatment at different time, different place, different outcome\n- [ ] Robustness to: alternative samples, measures, specifications, estimators\n- [ ] Threats to identification are explicitly discussed, not hidden\n- [ ] Oster (2019) bounds or Altonji et al. (2005) ratio test for selection on unobservables\n\n---\n\n## Common Rejection Reasons in Marketing Journals\n\n| Reason | Frequency | Prevention |\n|--------|-----------|------------|\n| Weak identification \u002F unconvincing instruments | Very High | Show first-stage F, discuss excludability, multiple instruments |\n| Endogeneity not adequately addressed | Very High | Explicit identification section; don't rely on fixed effects alone |\n| \"So what?\"—no substantive marketing implication | High | Concrete counterfactual; what changes in practice? |\n| Incremental over existing literature | High | Clear differentiation table; novel data or context |\n| Model not connected to data | Medium | Model should motivate specification; don't estimate a kitchen sink |\n| Mechanical application of method | Medium | Method serves the question; contribution is insight, not technique |\n\n---\n\n## 0-to-Draft Pipeline: Five-Stage Workflow\n\n### Stage 1: Substantive Problem & Positioning\n\n**Goal**: Identify the marketing problem, articulate the gap, select target journal.\n\n**Agent actions**:\n\n```\n1.1 Understand the marketing phenomenon\n    - Ask: What is the substantive marketing problem? What decision does it inform?\n    - Identify the core consumer\u002Ffirm trade-off\n    - Classify: Structural \u002F Reduced-form \u002F Experimental \u002F Conceptual\n\n1.2 Literature gap analysis\n    - Search for 10-15 closest papers in target journals\n    - Build a Gap Table: rows = key references, columns = dimensions (data, method, context, finding)\n    - Identify the empty cell this paper fills\n    - Output: draft gap table with explicit differentiation\n\n1.3 Journal selection\n    - Match paper profile to journal identity\n    - Consider: MktSci (structural + novel data) vs. JMR (identification + relevance) vs. JM (strategy + multi-method)\n    - Output: recommended journal + 1-2 fallback options\n\n1.4 Contribution statement\n    - One sentence: \"Using [data\u002Fmethod], we study [phenomenon] and find [key insight]. For marketers, this means [implication].\"\n    - Draft 3-5 numbered contributions\n    - Output: contribution list\n\n1.5 Writing plan\n    - Outline paper structure with estimated page allocation\n    - Identify reference files to load at each stage\n```\n\n**Reference to load**: `references\u002Fjournal-characteristics.md`\n\n### Stage 2: Model & Identification Design\n\n**Goal**: Specify consumer\u002Ffirm model and articulate empirical identification.\n\n**Agent actions**:\n\n```\n2.1 Consumer utility specification\n    - Specify indirect utility: u_ijt = f(price, characteristics, heterogeneity)\n    - For structural: random coefficients, outside good, market definition\n    - For reduced-form: outcome equation, treatment definition\n\n2.2 Firm behavior (if applicable)\n    - Pricing: Bertrand-Nash, uniform pricing, price discrimination\n    - Advertising, entry, product line decisions\n\n2.3 Identification strategy\n    - Source of exogenous variation: what quasi-experiment or instrument?\n    - Identifying assumptions and how they're tested\n    - Institutional details that make the strategy credible\n    - Output: identification narrative (~1 page)\n\n2.4 Data description\n    - Data sources, sample construction, summary statistics\n    - Key variables: construction, sources, limitations\n    - Output: §3 Data & Identification draft\n```\n\n**Reference to load**: `references\u002Fstructural-modeling.md` or `references\u002Fcausal-inference.md`\n\n### Stage 3: Estimation & Results\n\n**Goal**: Implement estimator, present results with diagnostics.\n\n**Agent actions**:\n\n```\n3.1 Estimator specification\n    - Structural: GMM objective, instruments, weighting matrix, optimization\n    - Reduced-form: regression specification, fixed effects, standard error clustering\n    - Experimental: ITT specification, randomization check\n\n3.2 Results presentation\n    - Main results table with diagnostics\n    - Economic significance: elasticities, WTP, treatment effect magnitude\n    - Not just statistical significance (p-values)\n    - Output: §4 Estimation \u002F Results draft\n\n3.3 Diagnostic tests\n    - First-stage F, overidentification J-test, DWH endogeneity test\n    - Balance table, attrition analysis, parallel trends\n```\n\n### Stage 4: Counterfactuals & Robustness\n\n**Goal**: Conduct policy simulations, demonstrate robustness, show mechanism.\n\n**Agent actions**:\n\n```\n4.1 Counterfactual simulations (structural papers)\n    - Merger: change ownership matrix, recompute equilibrium\n    - Tax\u002Fsubsidy: add per-unit tax, compute pass-through\n    - Product introduction\u002Fremoval: change choice set\n    - Report: compensating variation, profit changes, bootstrap CIs\n    - Output: §5 Counterfactuals draft\n\n4.2 Robustness analysis (all papers)\n    - Alternative DV, IV, sample, specification, estimator\n    - Placebo tests\n    - Oster bounds for selection on unobservables\n    - Output: §5 Robustness draft\n\n4.3 Heterogeneity \u002F Mechanism\n    - Who is most affected? (demographic, behavioral, contextual)\n    - Through what channel? (mediation, moderation, process evidence)\n```\n\n**Reference to load**: `references\u002Fstructural-modeling.md` (counterfactuals) or `references\u002Fcausal-inference.md` (robustness)\n\n### Stage 5: Writing & Assembly\n\n**Goal**: Write the full paper with marketing implications.\n\n**Agent actions**:\n\n```\n5.1 Introduction drafting\n    - P1: Motivating marketing phenomenon (real company, real numbers)\n    - P2: Why existing literature\u002Fapproaches fail\n    - P3: Our approach and identification strategy\n    - P4: Contributions (numbered, 3-5 items)\n    - P5: Paper organization\n\n5.2 Literature review\n    - Organize by substantive domain, not paper-by-paper\n    - Include Gap Table from Stage 1\n    - End each stream: what's known + what's missing\n\n5.3 Marketing implications\n    - What should a CMO\u002Fmarketing manager DO differently?\n    - Connect to specific results (robust to X, not just significant)\n    - Quantify where possible: \"A 10% increase in X leads to Y% change in sales\"\n    - Distinguish: statistical significance vs. economic significance vs. managerial significance\n\n5.4 Abstract\n    - Write LAST\n    - Substantive problem → Method → Key finding → Marketing implication\n    - Max 200 words for MktSci, JMR, JM, JCR\n\n5.5 Cross-section consistency check\n    - Contributions list ↔ body sections (1:1)\n    - All variables defined at first use\n    - All tables\u002Ffigures referenced in text\n```\n\n**References to load**: `references\u002Fwriting-patterns.md`, `references\u002Freviewer-expectations.md`\n\n---\n\n## Writing Patterns\n\n### Pattern 1: The Managerial Motivating Example\nOpen with a concrete marketing decision faced by a real company. \"In 2023, P&G allocated $7.1B to advertising across 40 brands, yet had no systematic way to measure cross-brand spillover effects...\"\n\n### Pattern 2: The Identification Narrative\nDevote an entire section to explaining the source of exogenous variation. The best marketing papers make the identification strategy feel like a detective story.\n\n### Pattern 3: The Elasticity Table\nFor demand estimation papers, the own- and cross-price elasticity matrix is the central result. Present a representative subset with standard errors.\n\n### Pattern 4: The Counterfactual Triplet\nFor each counterfactual: Baseline → Counterfactual → Difference. Always report consumer welfare (compensating variation) alongside firm profit.\n\n---\n\n## Venue-Specific Page Budgets\n\n| Journal | Submission Length | References | Appendix | Abstract |\n|---------|:---:|:---:|:---:|:---:|\n| Marketing Science | 35 pages | Unlimited | Unlimited | 200 words |\n| JMR | 40 pages | Unlimited | Unlimited | 200 words |\n| JM | 40 pages | Unlimited | Unlimited | 200 words |\n| JCR | 40 pages | Unlimited | Unlimited | 150 words |\n| JAMS | 40 pages | Unlimited | Unlimited | 200 words |\n| IJRM | 35 pages | Unlimited | Unlimited | 200 words |\n| QME | 40 pages | Unlimited | Unlimited | 200 words |\n| Marketing Letters | 20 pages | Unlimited | Limited | 150 words |\n\n---\n\n## Quick Checklist Before Submission\n\n- [ ] Target journal fits the paper's method-contribution profile\n- [ ] Identification strategy is clearly articulated and credible\n- [ ] First-stage (if IV) or parallel trends (if DID) convincingly shown\n- [ ] Results table: coefficients + SE + economic significance + diagnostics\n- [ ] Robustness: 8+ checks covering alternative measures, samples, specifications\n- [ ] Counterfactuals (if structural): baseline vs. counterfactual with welfare\n- [ ] Marketing implications: specific, actionable, connected to results\n- [ ] Introduction: motivating example + gap + numbered contributions\n- [ ] All citations verified programmatically (no hallucinated references)\n- [ ] Page budget within limits; long proofs\u002Fmaterials in appendix\n\n---\n\n## References\n\n- [references\u002Fjournal-characteristics.md](references\u002Fjournal-characteristics.md): Detailed per-journal characteristics, editorial philosophy, reviewer expectations\n- [references\u002Fstructural-modeling.md](references\u002Fstructural-modeling.md): BLP framework, demand estimation, supply side, counterfactuals, diagnostics\n- [references\u002Fcausal-inference.md](references\u002Fcausal-inference.md): DID, RDD, IV, synthetic control, matching—implementation and reporting\n- [references\u002Ffield-experiments.md](references\u002Ffield-experiments.md): Design, power analysis, pre-registration, CONSORT, analysis\n- [references\u002Fconjoint-analysis.md](references\u002Fconjoint-analysis.md): Fractional factorial design, HB estimation, WTP, validation\n- [references\u002Freviewer-expectations.md](references\u002Freviewer-expectations.md): What MktSci\u002FJMR\u002FJM\u002FJCR reviewers look for, common criticisms, rebuttal strategies\n- [references\u002Fwriting-patterns.md](references\u002Fwriting-patterns.md): Reusable writing patterns from published marketing science papers\n","# Marketing Science Paper Writing\n\n## Overview\n\nMarketing Science papers follow a distinct pipeline: **Substantive Problem → Consumer + Firm Model → Identification Strategy → Estimation\u002FExperiment → Counterfactual Analysis → Marketing Implications**. Unlike MS&E papers that prioritize analytical derivation, marketing papers are evaluated on the **credibility of the empirical identification** and the **managerial actionability of the counterfactual**.\n\nThis skill provides journal-specific guidance for 8 flagship marketing journals, covering structural modeling, causal inference, field experiments, conjoint analysis, and consumer analytics.\n\n**Core Principle**: A marketing paper is an **empirical argument about consumer or firm behavior with testable implications**. Every model, regression, and experiment must answer: \"What should a Chief Marketing Officer do differently because of this finding?\"\n\n---\n\n## When to Use This Skill\n\n- Drafting a paper targeting Marketing Science, JMR, JM, JCR, JAMS, IJRM, QME, or Marketing Letters\n- Designing a structural model of consumer demand (BLP, dynamic structural, discrete choice)\n- Planning identification strategy (IV, DID, RDD, field experiment, lab experiment)\n- Writing up conjoint analysis, demand estimation, or field experiment results\n- Conducting counterfactual policy simulations (merger, tax, product introduction)\n- Formulating substantive marketing implications from empirical results\n- Addressing endogeneity concerns in observational marketing data\n\n---\n\n## Journal Selection Quick Reference\n\n### Tier 1: Top Marketing Journals\n\n| Journal | Core Identity | Ideal Paper Profile |\n|---------|--------------|---------------------|\n| **Marketing Science** (MktSci) | Quantitative, model-driven, structural | Structural model + novel data + counterfactual showing substantive managerial impact. ~35 pages. |\n| **Journal of Marketing Research** (JMR) | Empirical rigor, identification credibility | Well-identified study (experiment\u002Fquasi-experiment) + clear managerial recommendations. ~40 pages. |\n| **Journal of Marketing** (JM) | Broad strategy, conceptual + empirical | Conceptual framework + multi-method validation; strong on marketing strategy. ~40 pages. |\n| **Journal of Consumer Research** (JCR) | Consumer psychology, behavioral | Theory-driven lab\u002Ffield experiments testing psychological mechanisms. ~40 pages. |\n\n### Tier 2: Strong Field Journals\n\n| Journal | Core Identity | Ideal Paper Profile |\n|---------|--------------|---------------------|\n| **JAMS** (J. Academy of Marketing Science) | Broad scope, reviews valued | Conceptual + empirical; strong review papers; AMA-affiliated |\n| **IJRM** (Int. J. Research in Marketing) | European tradition, diverse methods | All methods welcomed; strong special issue culture |\n| **QME** (Quantitative Marketing & Economics) | Structural IO approach | Rigorous structural econometrics; smaller volume, higher technical bar |\n| **Marketing Letters** | Short, sharp empirical | Concise empirical contributions (~20 pages); replication-friendly |\n\n### Decision Flowchart\n\n```\nWhat is your primary method?\n├── Structural econometric model (BLP, dynamic) → Marketing Science, QME\n├── Field experiment (A\u002FB test, RCT) → JMR, Marketing Science\n├── Lab experiment (psychological mechanism) → JCR, JMR\n├── Multi-method empirical (survey + secondary) → JM, JAMS\n├── Conceptual\u002Ftheoretical framework → JM, JAMS, JCR\n└── Short empirical note → Marketing Letters\n```\n\n---\n\n## The Marketing Paper Structure\n\nUnlike IMRAD or MS&E papers, marketing papers have a distinctive structure driven by the identification narrative:\n\n| Section | Typical Pages | Key Content |\n|---------|--------------|-------------|\n| Abstract | 150-200 words | Substantive problem, method, key finding, substantive marketing implication |\n| S1 Introduction | 2-3 pages | Motivating marketing phenomenon → Research gap → Identification approach → Contributions |\n| S2 Conceptual Framework \u002F Model | 3-6 pages | Consumer utility specification, firm objective, market equilibrium, hypotheses |\n| S3 Data & Identification | 4-8 pages | Data sources, sample construction, identification strategy, institutional context |\n| S4 Estimation \u002F Experiment Design | 3-6 pages | Estimator specification, experimental protocol, diagnostic tests, main results |\n| S5 Counterfactuals \u002F Robustness | 3-5 pages | Policy simulations, heterogeneity analysis, alternative specifications, placebo tests |\n| S6 Discussion & Marketing Implications | 1-2 pages | What should marketers DO differently? Theoretical contributions. Limitations. |\n| References | — | 40-80 papers typical |\n| Appendix | Unlimited | Additional robustness, alternative specifications, simulation details |\n\n---\n\n## Methodology Guide by Approach\n\n### 1. Structural Econometric Modeling\n\nSee [references\u002Fstructural-modeling.md](references\u002Fstructural-modeling.md) for comprehensive guidance.\n\n**The BLP Framework** (Berry-Levinsohn-Pakes, 1995)—the workhorse for aggregate demand estimation:\n\n$u_{ijt} = \\alpha_i p_{jt} + \\beta_i x_{jt} + \\xi_{jt} + \\varepsilon_{ijt}$\n\nKey elements:\n- Consumer heterogeneity via random coefficients ($\\alpha_i, \\beta_i$)\n- Price endogeneity addressed via instruments (cost shifters, Hausman instruments, BLP differentiation IVs)\n- Demand inversion via contraction mapping: $\\delta_{jt} \\leftarrow \\delta_{jt} + \\ln s_{jt} - \\ln s_{jt}(\\delta, \\theta)$\n- Supply side: Bertrand-Nash FOCs provide additional GMM moments\n- Counterfactuals: merger simulation, tax incidence, product entry\u002Fexit\n\n**Dynamic Structural Models**:\n- State variables: brand loyalty, inventory, advertising stock, learning\n- Solution: nested fixed point (Rust) or CCP (Hotz-Miller)\n- Applications: brand choice dynamics, new product diffusion, customer relationship management\n\n### 2. Causal Inference \u002F Reduced-Form\n\nSee [references\u002Fcausal-inference.md](references\u002Fcausal-inference.md) for comprehensive guidance.\n\n**Primary Designs in Marketing**:\n\n| Design | Typical Application | Key Reference |\n|--------|-------------------|---------------|\n| **Difference-in-Differences** | Ad campaign effects, store entry, policy changes | Callaway & Sant'Anna (2021); Sun & Abraham (2021) |\n| **Regression Discontinuity** | Price thresholds, eligibility cutoffs, geographic boundaries | Calonico et al. (2014) |\n| **Instrumental Variables** | Price endogeneity, advertising endogeneity | Angrist & Krueger (2001) |\n| **Synthetic Control** | Single-market interventions | Abadie et al. (2010) |\n\n**The Identification Narrative**: Every marketing paper must clearly articulate:\n1. The causal relationship of interest\n2. The source of endogenous variation\n3. The source of exogenous variation (the \"natural experiment\")\n4. The identifying assumptions\n5. How assumptions are tested (balance tests, placebo tests, falsification)\n\n### 3. Field Experiments\n\nSee [references\u002Ffield-experiments.md](references\u002Ffield-experiments.md) for comprehensive guidance.\n\n**Design Elements**:\n- Randomization unit: individual customer, store, DMA (Designated Market Area)\n- Stratification and blocking for balance\n- Power analysis to justify sample size\n- Pre-registration (AEA Registry, AsPredicted, OSF)\n\n**Reporting Standards**:\n- CONSORT-style flow diagram showing participant flow\n- Balance table comparing treatment\u002Fcontrol on pre-treatment characteristics\n- Intent-to-treat (ITT) as primary estimate\n- Treatment-on-the-treated (TOT) via instrumental variables for non-compliance\n- Multiple testing correction (Bonferroni, Holm, or Benjamini-Hochberg)\n\n### 4. Conjoint Analysis\n\nSee [references\u002Fconjoint-analysis.md](references\u002Fconjoint-analysis.md) for comprehensive guidance.\n\n**Design**: Fractional factorial, random designs; incentive-aligned via BDM mechanism\n**Estimation**: Hierarchical Bayes multinomial logit; WTP from part-worths\n**Validation**: Holdout prediction accuracy; face validity of attribute importances\n\n---\n\n## Identification: The Make-or-Break Dimension\n\nMarketing Science and JMR reviewers prioritize identification credibility above all else. A paper without a clearly articulated identification strategy is desk-rejected.\n\n### Identification Checklist\n\n- [ ] Source of exogenous variation is clearly described with institutional detail\n- [ ] First-stage is strong (F > 10 for IV; visual jump for RDD)\n- [ ] Parallel pre-trends are shown visually (event study plot for DID)\n- [ ] Balance table compares treatment\u002Fcontrol on pre-treatment observables\n- [ ] Placebo\u002Ffalsification tests: fake treatment at different time, different place, different outcome\n- [ ] Robustness to: alternative samples, measures, specifications, estimators\n- [ ] Threats to identification are explicitly discussed, not hidden\n- [ ] Oster (2019) bounds or Altonji et al. (2005) ratio test for selection on unobservables\n\n---\n\n## Common Rejection Reasons in Marketing Journals\n\n| Reason | Frequency | Prevention |\n|--------|-----------|------------|\n| Weak identification \u002F unconvincing instruments | Very High | Show first-stage F, discuss excludability, multiple instruments |\n| Endogeneity not adequately addressed | Very High | Explicit identification section; don't rely on fixed effects alone |\n| \"So what?\"—no substantive marketing implication | High | Concrete counterfactual; what changes in practice? |\n| Incremental over existing literature | High | Clear differentiation table; novel data or context |\n| Model not connected to data | Medium | Model should motivate specification; don't estimate a kitchen sink |\n| Mechanical application of method | Medium | Method serves the question; contribution is insight, not technique |\n\n---\n\n## 0-to-Draft Pipeline: Five-Stage Workflow\n\n### Stage 1: Substantive Problem & Positioning\n\n**Goal**: Identify the marketing problem, articulate the gap, select target journal.\n\n**Agent actions**:\n\n```\n1.1 Understand the marketing phenomenon\n    - Ask: What is the substantive marketing problem? What decision does it inform?\n    - Identify the core consumer\u002Ffirm trade-off\n    - Classify: Structural \u002F Reduced-form \u002F Experimental \u002F Conceptual\n\n1.2 Literature gap analysis\n    - Search for 10-15 closest papers in target journals\n    - Build a Gap Table: rows = key references, columns = dimensions (data, method, context, finding)\n    - Identify the empty cell this paper fills\n    - Output: draft gap table with explicit differentiation\n\n1.3 Journal selection\n    - Match paper profile to journal identity\n    - Consider: MktSci (structural + novel data) vs. JMR (identification + relevance) vs. JM (strategy + multi-method)\n    - Output: recommended journal + 1-2 fallback options\n\n1.4 Contribution statement\n    - One sentence: \"Using [data\u002Fmethod], we study [phenomenon] and find [key insight]. For marketers, this means [implication].\"\n    - Draft 3-5 numbered contributions\n    - Output: contribution list\n\n1.5 Writing plan\n    - Outline paper structure with estimated page allocation\n    - Identify reference files to load at each stage\n```\n\n**Reference to load**: `references\u002Fjournal-characteristics.md`\n\n### Stage 2: Model & Identification Design\n\n**Goal**: Specify consumer\u002Ffirm model and articulate empirical identification.\n\n**Agent actions**:\n\n```\n2.1 Consumer utility specification\n    - Specify indirect utility: u_ijt = f(price, characteristics, heterogeneity)\n    - For structural: random coefficients, outside good, market definition\n    - For reduced-form: outcome equation, treatment definition\n\n2.2 Firm behavior (if applicable)\n    - Pricing: Bertrand-Nash, uniform pricing, price discrimination\n    - Advertising, entry, product line decisions\n\n2.3 Identification strategy\n    - Source of exogenous variation: what quasi-experiment or instrument?\n    - Identifying assumptions and how they're tested\n    - Institutional details that make the strategy credible\n    - Output: identification narrative (~1 page)\n\n2.4 Data description\n    - Data sources, sample construction, summary statistics\n    - Key variables: construction, sources, limitations\n    - Output: §3 Data & Identification draft\n```\n\n**Reference to load**: `references\u002Fstructural-modeling.md` or `references\u002Fcausal-inference.md`\n\n### Stage 3: Estimation & Results\n\n**Goal**: Implement estimator, present results with diagnostics.\n\n**Agent actions**:\n\n```\n3.1 Estimator specification\n    - Structural: GMM objective, instruments, weighting matrix, optimization\n    - Reduced-form: regression specification, fixed effects, standard error clustering\n    - Experimental: ITT specification, randomization check\n\n3.2 Results presentation\n    - Main results table with diagnostics\n    - Economic significance: elasticities, WTP, treatment effect magnitude\n    - Not just statistical significance (p-values)\n    - Output: §4 Estimation \u002F Results draft\n\n3.3 Diagnostic tests\n    - First-stage F, overidentification J-test, DWH endogeneity test\n    - Balance table, attrition analysis, parallel trends\n```\n\n### Stage 4: Counterfactuals & Robustness\n\n**Goal**: Conduct policy simulations, demonstrate robustness, show mechanism.\n\n**Agent actions**:\n\n```\n4.1 Counterfactual simulations (structural papers)\n    - Merger: change ownership matrix, recompute equilibrium\n    - Tax\u002Fsubsidy: add per-unit tax, compute pass-through\n    - Product introduction\u002Fremoval: change choice set\n    - Report: compensating variation, profit changes, bootstrap CIs\n    - Output: §5 Counterfactuals draft\n\n4.2 Robustness analysis (all papers)\n    - Alternative DV, IV, sample, specification, estimator\n    - Placebo tests\n    - Oster bounds for selection on unobservables\n    - Output: §5 Robustness draft\n\n4.3 Heterogeneity \u002F Mechanism\n    - Who is most affected? (demographic, behavioral, contextual)\n    - Through what channel? (mediation, moderation, process evidence)\n```\n\n**Reference to load**: `references\u002Fstructural-modeling.md` (counterfactuals) or `references\u002Fcausal-inference.md` (robustness)\n\n### Stage 5: Writing & Assembly\n\n**Goal**: Write the full paper with marketing implications.\n\n**Agent actions**:\n\n```\n5.1 Introduction drafting\n    - P1: Motivating marketing phenomenon (real company, real numbers)\n    - P2: Why existing literature\u002Fapproaches fail\n    - P3: Our approach and identification strategy\n    - P4: Contributions (numbered, 3-5 items)\n    - P5: Paper organization\n\n5.2 Literature review\n    - Organize by substantive domain, not paper-by-paper\n    - Include Gap Table from Stage 1\n    - End each stream: what's known + what's missing\n\n5.3 Marketing implications\n    - What should a CMO\u002Fmarketing manager DO differently?\n    - Connect to specific results (robust to X, not just significant)\n    - Quantify where possible: \"A 10% increase in X leads to Y% change in sales\"\n    - Distinguish: statistical significance vs. economic significance vs. managerial significance\n\n5.4 Abstract\n    - Write LAST\n    - Substantive problem → Method → Key finding → Marketing implication\n    - Max 200 words for MktSci, JMR, JM, JCR\n\n5.5 Cross-section consistency check\n    - Contributions list ↔ body sections (1:1)\n    - All variables defined at first use\n    - All tables\u002Ffigures referenced in text\n```\n\n**References to load**: `references\u002Fwriting-patterns.md`, `references\u002Freviewer-expectations.md`\n\n---\n\n## Writing Patterns\n\n### Pattern 1: The Managerial Motivating Example\nOpen with a concrete marketing decision faced by a real company. \"In 2023, P&G allocated $7.1B to advertising across 40 brands, yet had no systematic way to measure cross-brand spillover effects...\"\n\n### Pattern 2: The Identification Narrative\nDevote an entire section to explaining the source of exogenous variation. The best marketing papers make the identification strategy feel like a detective story.\n\n### Pattern 3: The Elasticity Table\nFor demand estimation papers, the own- and cross-price elasticity matrix is the central result. Present a representative subset with standard errors.\n\n### Pattern 4: The Counterfactual Triplet\nFor each counterfactual: Baseline → Counterfactual → Difference. Always report consumer welfare (compensating variation) alongside firm profit.\n\n---\n\n## Venue-Specific Page Budgets\n\n| Journal | Submission Length | References | Appendix | Abstract |\n|---------|:---:|:---:|:---:|:---:|\n| Marketing Science | 35 pages | Unlimited | Unlimited | 200 words |\n| JMR | 40 pages | Unlimited | Unlimited | 200 words |\n| JM | 40 pages | Unlimited | Unlimited | 200 words |\n| JCR | 40 pages | Unlimited | Unlimited | 150 words |\n| JAMS | 40 pages | Unlimited | Unlimited | 200 words |\n| IJRM | 35 pages | Unlimited | Unlimited | 200 words |\n| QME | 40 pages | Unlimited | Unlimited | 200 words |\n| Marketing Letters | 20 pages | Unlimited | Limited | 150 words |\n\n---\n\n## Quick Checklist Before Submission\n\n- [ ] Target journal fits the paper's method-contribution profile\n- [ ] Identification strategy is clearly articulated and credible\n- [ ] First-stage (if IV) or parallel trends (if DID) convincingly shown\n- [ ] Results table: coefficients + SE + economic significance + diagnostics\n- [ ] Robustness: 8+ checks covering alternative measures, samples, specifications\n- [ ] Counterfactuals (if structural): baseline vs. counterfactual with welfare\n- [ ] Marketing implications: specific, actionable, connected to results\n- [ ] Introduction: motivating example + gap + numbered contributions\n- [ ] All citations verified programmatically (no hallucinated references)\n- [ ] Page budget within limits; long proofs\u002Fmaterials in appendix\n\n---\n\n## References\n\n- [references\u002Fjournal-characteristics.md](references\u002Fjournal-characteristics.md): Detailed per-journal characteristics, editorial philosophy, reviewer expectations\n- [references\u002Fstructural-modeling.md](references\u002Fstructural-modeling.md): BLP framework, demand estimation, supply side, counterfactuals, diagnostics\n- [references\u002Fcausal-inference.md](references\u002Fcausal-inference.md): DID, RDD, IV, synthetic control, matching—implementation and reporting\n- [references\u002Ffield-experiments.md](references\u002Ffield-experiments.md): Design, power analysis, pre-registration, CONSORT, analysis\n- [references\u002Fconjoint-analysis.md](references\u002Fconjoint-analysis.md): Fractional factorial design, HB estimation, WTP, validation\n- [references\u002Freviewer-expectations.md](references\u002Freviewer-expectations.md): What MktSci\u002FJMR\u002FJM\u002FJCR reviewers look for, common criticisms, rebuttal strategies\n- [references\u002Fwriting-patterns.md](references\u002Fwriting-patterns.md): Reusable writing patterns from published marketing science papers\n",{},"master","9a4b2db21fc4b217136ae4a0e6faad42d179ca03","Awesome-Journal-Skills-main\u002Fmarketing-science-writing-main",36,"https:\u002F\u002Fgithub.com\u002Ffranklee16\u002Facademic-research-skills","https:\u002F\u002Fgithub.com\u002Ffranklee16","https:\u002F\u002Fsites.google.com\u002Fview\u002Ffrankweikaili\u002Fhome","Associate Professor of Finance at City University of Hong Kong - empirical asset pricing, behavioral finance, ESG. 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Use when a user asks to recover or recreate chart data, extract scatter points, bars, printed pie\u002Fdonut labels, or aligned memberships, check a PDF chart, assess digitization accuracy, validate against source data, or improve a chart-digitization workflow from corrected examples.","https:\u002F\u002Fgithub.com\u002Ffranklee16\u002Facademic-research-skills\u002Fblob\u002Fmaster\u002Fvisualization\u002Fthu-digitizer-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ffranklee16\u002Facademic-research-skills\u002Fblob\u002Fmaster\u002Fvisualization\u002Fthu-digitizer-main\u002FSKILL.md",[137,140],{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":141,"categoryName":142,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师",[144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163],"thu digitizer","cross-validation","line","Evaluation","workflow","data","inspection","regression","official","labels","pixel","improve","files","PDF","error","images","vector","chart","compact","when",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":176,"publishedAt":41},"01M71G4XJWMJST9K8X244GXH71","scientific-visualization","Meta-skill for publication-ready figures. 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For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffranklee16\u002Facademic-research-skills\u002Fblob\u002Fmaster\u002Fvisualization\u002Fscientific-visualization\u002FSKILL.md",[170,173],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[177,178,179,180,181,182,183,163,184,185,186,187,188,189,158,190,191,192,193,194],"scientific visualization","scientific","visualization","Meta","plotly","safe","journal 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的案例研究规范，拆成\"主问题—追问—探测\"，保障与研究问题对齐、可信度（trustworthiness）与三角验证就绪，标注方法论依据与权威出处，产出研究者版 + 现场详版\u002F简版。不要用于：量化问卷 \u002F 量表设计（走 questionnaire-design）、求职面试、以传播或叙事为目的的人物访谈（媒体\u002F播客\u002F记者深度报道\u002F纪录片\u002F口述史，非以回答研究问题为目的）、访谈录音转写、焦点小组\u002F座谈会提纲（群体互动动态与主持伦理不同，本 skill 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interview-guide-design）、只设计单一问卷、把现成工具做成网页、访谈转写、正式数据分析，或在没有研究问题的情况下机械拼接两套题目。","https:\u002F\u002Fgithub.com\u002Ffranklee16\u002Facademic-research-skills\u002Fblob\u002Fmaster\u002Frunning-surveys-experiments\u002Fmixed-methods-instrument-design-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ffranklee16\u002Facademic-research-skills\u002Fblob\u002Fmaster\u002Frunning-surveys-experiments\u002Fmixed-methods-instrument-design-main\u002FSKILL.md",[256,257,258,259],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":232,"categoryName":233,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":236,"categoryName":237,"taxonomyType":29,"taxonomyTypeDesc":30},[261,262,245,263,264,265,266,243,267,246,268,269,244],"methods","mixed methods instrument design","instrument","testing","数据","Word","survey","data 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